Etude des séries temporelles en imagerie satellitaire SAR pour la détection automatique de changements

Abstract : This PhD thesis presents the MIMOSA (Method for generalIzed Means Ordered Series Analysis) change detection methood. This new technique can automatically detect changes between SAR image pairs or within time series. Indeed, thanks to the temporeal means, the number of involved images doesn’t matters because only two different means are compared to detect the changes (for example, the geometric and quadratic means). Thus, large data volumes can be processed easily, since the useful information is condensed within the temporal means. The only change detection parameter is the false alarm rate that will be MIMOSA method are very good compared to other methods. Several tests have been performed in order to quantify the robustness of the method facing the most common problems, like image misregistration or radiometric calibration errors. A graphical user interface has also been developed for MIMOSA, including many useful tools to prepare and process SAR data, but also several analyse tools.
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Guillaume Quin. Etude des séries temporelles en imagerie satellitaire SAR pour la détection automatique de changements. Traitement du signal et de l'image [eess.SP]. Télécom ParisTech, 2014. Français. ⟨NNT : 2014ENST0003⟩. ⟨tel-02127637⟩

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